
# coding: utf-8

# In[1]:


import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

#导入数据
data_dir = '/tmp/tensorflow/mnist/input_data'
mnist = input_data.read_data_sets(data_dir, one_hot=True)


# In[2]:


#定义卷积参数
INPUT_NODE = 784
OUTPUT_NODE = 10

IMAGE_SIZE = 28
NUM_CHANNELS = 1
NUM_LABELS = 10

CONV1_DEEP = 32
CONV1_SIZE = 5

CONV2_DEEP = 64
CONV2_SIZE = 5

FC_SIZE = 512


# In[3]:


def inference(input_tensor, train, regularizer):
    with tf.variable_scope('layer1-conv1', reuse=tf.AUTO_REUSE):
        conv1_weights = tf.get_variable(
            "weight", [CONV1_SIZE, CONV1_SIZE, NUM_CHANNELS, CONV1_DEEP],
        initializer=tf.truncated_normal_initializer(stddev=0.1))
        conv1_biases = tf.get_variable("bias", [CONV1_DEEP], initializer=tf.constant_initializer(0.0))
        conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')
        relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))

    with tf.name_scope("layer2-pool1"):
        pool1 = tf.nn.max_pool(relu1, ksize = [1,2,2,1],strides=[1,2,2,1],padding="SAME")

    with tf.variable_scope("layer3-conv2", reuse=tf.AUTO_REUSE):
        conv2_weights = tf.get_variable(
            "weight", [CONV2_SIZE, CONV2_SIZE, CONV1_DEEP, CONV2_DEEP],
        initializer=tf.truncated_normal_initializer(stddev=0.1))
        conv2_biases = tf.get_variable("bias", [CONV2_DEEP], initializer=tf.constant_initializer(0.0))
        conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')
        relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))

    with tf.name_scope("layer4-pool2",):
        pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
        pool_shape = pool2.get_shape().as_list()
        nodes = pool_shape[1] * pool_shape[2] * pool_shape[3]
        reshaped = tf.reshape(pool2, [pool_shape[0], nodes])

    with tf.variable_scope('layer5-fc1', reuse=tf.AUTO_REUSE):
        fc1_weights = tf.get_variable("weight", [nodes, FC_SIZE],
        initializer=tf.truncated_normal_initializer(stddev=0.1))
        if regularizer != None: tf.add_to_collection('losses', regularizer(fc1_weights))
        fc1_biases = tf.get_variable("bias", [FC_SIZE], initializer=tf.constant_initializer(0.1))

        fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases)
        if train: fc1 = tf.nn.dropout(fc1, 0.5)

    with tf.variable_scope('layer6-fc2', reuse=tf.AUTO_REUSE):
        fc2_weights = tf.get_variable("weight", [FC_SIZE, NUM_LABELS],
        initializer=tf.truncated_normal_initializer(stddev=0.1))
        if regularizer != None: tf.add_to_collection('losses', regularizer(fc2_weights))
        fc2_biases = tf.get_variable("bias", [NUM_LABELS], initializer=tf.constant_initializer(0.1))
        logit = tf.matmul(fc1, fc2_weights) + fc2_biases

    return logit


# In[ ]:


BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.01
LEARNING_RATE_DECAY = 0.99
REGULARIZATION_RATE = 0.0001
TRAINING_STEPS = 10000
MOVING_AVERAGE_DECAY = 0.99

# 定义输出为4维矩阵的placeholder
x = tf.placeholder(tf.float32, [
            BATCH_SIZE,
            IMAGE_SIZE,
            IMAGE_SIZE,
            NUM_CHANNELS],
        name='x-input')
y_ = tf.placeholder(tf.float32, [None, OUTPUT_NODE], name='y-input')
    
regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)
y = inference(x,False,regularizer)
global_step = tf.Variable(0, trainable=False)

# 定义损失函数、学习率、滑动平均操作以及训练过程。
variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
cross_entropy_mean = tf.reduce_mean(cross_entropy)
loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))
learning_rate = tf.train.exponential_decay(
    LEARNING_RATE_BASE,
    global_step,
    mnist.train.num_examples / BATCH_SIZE, LEARNING_RATE_DECAY,
    staircase=True)

train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)
with tf.control_dependencies([train_step, variables_averages_op]):
    train_op = tf.no_op(name='train')
    
#正确率
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        
# 初始化TensorFlow持久化类。
saver = tf.train.Saver()
with tf.Session() as sess:
    tf.global_variables_initializer().run()
    for i in range(TRAINING_STEPS):
        xs, ys = mnist.train.next_batch(BATCH_SIZE)

        reshaped_xs = np.reshape(xs, (
                BATCH_SIZE,
                IMAGE_SIZE,
                IMAGE_SIZE,
                NUM_CHANNELS))
        _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: reshaped_xs, y_: ys})

        if i % 1000 == 0:
            validate_acc = sess.run(accuracy, feed_dict = {x: reshaped_xs, y_: ys})
            print("After %d training steps, validation accuracy using average model is %g " % (i, validate_acc))
            

